




'''simple docstring'''



import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCAmelCase_							:	Optional[Any]	     =		random.Random()

if is_torch_available():
     import torch







def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  Optional[int]=1.0 ,						_lowerCamelCase				:  Dict=None ,						_lowerCamelCase				:  Union[str, Any]=None						)-> Any:




     '''simple docstring'''





     if rng is None:
          __snake_case            =	global_rng

     __snake_case            =	[]
     for batch_idx in range(shape[0]						):
          values.append([]						)
          for _ in range(shape[1]						):
               values[-1].append(rng.random() * scale						)

     return values





class      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=400     ,	__SCREAMING_SNAKE_CASE=2000     ,	__SCREAMING_SNAKE_CASE=1     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=1_6000     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	min_seq_length
            __snake_case            =	max_seq_length
            __snake_case            =	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
            __snake_case            =	feature_size
            __snake_case            =	padding_value
            __snake_case            =	sampling_rate
            __snake_case            =	return_attention_mask
            __snake_case            =	do_normalize


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            return {
                "feature_size": self.feature_size,
                "padding_value": self.padding_value,
                "sampling_rate": self.sampling_rate,
                "return_attention_mask": self.return_attention_mask,
                "do_normalize": self.do_normalize,
            }




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=False	)   ->	str:



            '''simple docstring'''




            def _flatten(__SCREAMING_SNAKE_CASE	):
                 return list(itertools.chain(*SCREAMING_SNAKE_CASE_	)	)

            if equal_length:
                 __snake_case            =	floats_list((self.batch_size, self.max_seq_length)	)
            else:
                 # make sure that inputs increase in size
                 __snake_case            =	[
                     _flatten(floats_list((x, self.feature_size)	)	)
                     for x in range(self.min_seq_length     ,	self.max_seq_length     ,	self.seq_length_diff	)
                 ]

            if numpify:
                 __snake_case            =	[np.asarray(SCREAMING_SNAKE_CASE_	) for x in speech_inputs]

            return speech_inputs





@require_torch
@require_torchaudio
class      lowerCAmelCase (			_lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   List[Any]										=      ASTFeatureExtractor


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	ASTFeatureExtractionTester(self	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
            # create three inputs of length 800, 1000, and 1200
            __snake_case            =	[floats_list((1, x)	)[0] for x in range(800     ,	1400     ,	200	)]
            __snake_case            =	[np.asarray(SCREAMING_SNAKE_CASE_	) for speech_input in speech_inputs]

            # Test not batched input
            __snake_case            =	feat_extract(speech_inputs[0]     ,	return_tensors='''np'''	).input_values
            __snake_case            =	feat_extract(np_speech_inputs[0]     ,	return_tensors='''np'''	).input_values
            self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-3	)	)

            # Test batched
            __snake_case            =	feat_extract(SCREAMING_SNAKE_CASE_     ,	padding=SCREAMING_SNAKE_CASE_     ,	return_tensors='''np'''	).input_values
            __snake_case            =	feat_extract(SCREAMING_SNAKE_CASE_     ,	padding=SCREAMING_SNAKE_CASE_     ,	return_tensors='''np'''	).input_values
            for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_	):
                 self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-3	)	)

            # Test 2-D numpy arrays are batched.
            __snake_case            =	[floats_list((1, x)	)[0] for x in (800, 800, 800)]
            __snake_case            =	np.asarray(SCREAMING_SNAKE_CASE_	)
            __snake_case            =	feat_extract(SCREAMING_SNAKE_CASE_     ,	return_tensors='''np'''	).input_values
            __snake_case            =	feat_extract(SCREAMING_SNAKE_CASE_     ,	return_tensors='''np'''	).input_values
            for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_	):
                 self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-3	)	)


       @require_torch
       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            import torch

            __snake_case            =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
            __snake_case            =	np.random.rand(100	).astype(np.floataa	)
            __snake_case            =	np_speech_inputs.tolist()

            for inputs in [py_speech_inputs, np_speech_inputs]:
                 __snake_case            =	feature_extractor.pad([{'''input_values''': inputs}]     ,	return_tensors='''np'''	)
                 self.assertTrue(np_processed.input_values.dtype == np.floataa	)
                 __snake_case            =	feature_extractor.pad([{'''input_values''': inputs}]     ,	return_tensors='''pt'''	)
                 self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            from datasets import load_dataset

            __snake_case            =	load_dataset('''hf-internal-testing/librispeech_asr_dummy'''     ,	'''clean'''     ,	split='''validation'''	)
            # automatic decoding with librispeech
            __snake_case            =	ds.sort('''id'''	).select(range(SCREAMING_SNAKE_CASE_	)	)[:num_samples]["""audio"""]

            return [x["array"] for x in speech_samples]




       @require_torch
       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	torch.tensor(
                [-0.9_894, -1.2_776, -0.9_066, -1.2_776, -0.9_349, -1.2_609, -1.0_386, -1.2_776,
                 -1.1_561, -1.2_776, -1.2_052, -1.2_723, -1.2_190, -1.2_132, -1.2_776, -1.1_133,
                 -1.1_953, -1.1_343, -1.1_584, -1.2_203, -1.1_770, -1.2_474, -1.2_381, -1.1_936,
                 -0.9_270, -0.8_317, -0.8_049, -0.7_706, -0.7_565, -0.7_869]	)
            # fmt: on

            __snake_case            =	self._load_datasamples(1	)
            __snake_case            =	ASTFeatureExtractor()
            __snake_case            =	feature_extractor(SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''	).input_values
            self.assertEquals(input_values.shape     ,	(1, 1024, 128)	)
            self.assertTrue(torch.allclose(input_values[0, 0, :30]     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-4	)	)











def  lowerCAmelCase_	(lowerCAmelCase__:		Union[str, Any]		,						lowerCAmelCase__:		Optional[int]  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     0
			UpperCAmelCase_:		Tuple							      =     len(lowerCAmelCase__  ) - 1

			while left <= right:
						# avoid divided by 0 during interpolation
						if sorted_collection[left] == sorted_collection[right]:
									if sorted_collection[left] == item:
												return left
									else:
												return None

						UpperCAmelCase_:		Dict							      =     left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(lowerCAmelCase__  ):
									return None

						UpperCAmelCase_:		str							      =     sorted_collection[point]
						if current_item == item:
									return point
						else:
									if point < left:
												UpperCAmelCase_:		int							      =     left
												UpperCAmelCase_:		str							      =     point
									elif point > right:
												UpperCAmelCase_:		List[str]							      =     right
												UpperCAmelCase_:		Optional[int]							      =     point
									else:
												if item < current_item:
															UpperCAmelCase_:		Optional[Any]							      =     point - 1
												else:
															UpperCAmelCase_:		Any							      =     point + 1
			return None







def  lowerCAmelCase_	(lowerCAmelCase__:		Dict		,						lowerCAmelCase__:		List[Any]		,						lowerCAmelCase__:		Any		,						lowerCAmelCase__:		Any  ):





			"""simple docstring"""


			if sorted_collection[left] == sorted_collection[right]:
						if sorted_collection[left] == item:
									return left
						else:
									return None

			UpperCAmelCase_:		Union[str, Any]							      =     left + ((item - sorted_collection[left]) * (right - left)) // (
			    sorted_collection[right] - sorted_collection[left]
			)

			# out of range check
			if point < 0 or point >= len(lowerCAmelCase__  ):
						return None

			if sorted_collection[point] == item:
						return point
			elif point < left:
						return interpolation_search_by_recursion(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )
			elif point > right:
						return interpolation_search_by_recursion(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )
			else:
						if sorted_collection[point] > item:
									return interpolation_search_by_recursion(
									    lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						point - 1  )
						else:
									return interpolation_search_by_recursion(
									    lowerCAmelCase__		,						lowerCAmelCase__		,						point + 1		,						lowerCAmelCase__  )







def  lowerCAmelCase_	(lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			if collection != sorted(lowerCAmelCase__  ):
						raise ValueError("""Collection must be ascending sorted"""  )
			return True


if __name__ == "__main__":
	import sys
	a      :  Optional[Any]			     =			0
	if debug == 1:
		a      :  int			     =			[10, 30, 40, 45, 50, 66, 77, 93]
		try:
			__assert_sorted(collection)
		except ValueError:
			sys.exit('Sequence must be ascending sorted to apply interpolation search')
		a      :  int			     =			67

	a      :  Any			     =			interpolation_search(collection, target)
	if result is not None:
		print(F'''{target} found at positions: {result}''')
	else:
		print('Not found')






